Areas of expertise

  • Autonomous Systems
  • Computing, Simulation & Modelling
  • Ergonomics, Human Factors, Driver Safety
  • Human Factors
  • Systems Engineering
  • Vehicle Engineering & Mobility


Dr. Yang Xing received his Ph.D. degree from Cranfield University in July 2018. He obtained his MSc degree with distinction in Control Systems from the University of Sheffield in 2014. Before joining Cranfield University in 2021, Dr Yang Xing worked as a research associate with the Department of Computer Science at the University of Oxford from 2020 to 2021, and a research fellow with the Department of Mechanical and Aerospace Engineering, at Nanyang Technological University from 2019 to 2020.

Dr. Yang Xing serves as the lab director of the HUMAX Lab at Cranfield University, which is focused on the design, testing, and validation of human-centred hybrid autonomous vehicles and transportation systems.His research interest focusses on applied artificial intelligence on human-autonomy collaboration and human-centred autonomous vehicles. He has contributed 2 books and over 90 papers on high-quality peer-review journals and conferences (including several ESI highly cited papers).

Dr. Yang Xing is a senior member of IEEE. He serves as an Associate Editor in IEEE Trans. on Intelligent Vehicles, IEEE Trans. on Neural Networks and Learning Systems, Highlight of Vehicles, and Review Editor in Frontiers in Mechanical Engineering. He was a Guest Editor-in-Chief/Editor in IEEE Internet of Things Journal, Mechanical Systems and Signal Processing, and IEEE Intelligent Transportation Magazine, etc. He was session chair/co-chair on IEEE SWC 2023, IEEE MFI 2022, IEEE SMC 2020, IFAC Workshop on CPHS 2020, and IEEE IV 2018, etc. He won the Best Workshop/Special Session Paper on IEEE IV 2018, Best Paper on China National Intelligence Technology Conference 2019, and IEEE Outstanding Service Award on IEEE Smart World Congress 2023.

Research opportunities

Currently I'm particular focus on the following research areas:

1. Cognitive and human-centred autonomous systems, theories and technologies in trustworthy and explainable AI for human-autonomy collaboration framework and interface design.

2. Computer vision for human behaviour modelling (including computational approaches for human situation awareness, intention, emotion, and workload).

3. Computer vision for autonomous vehicles (including segmentation, object detection, and generative visual augmentation) with multimodal foundation models and large language models.

4. Applied machine learning/deep learning theories, design, and evaluation for safer and sustainable autonomous vehicles (air/ground) and intelligent transportation systems.


Royal Society







Articles In Journals

Conference Papers